Numbering Code	G-GAIS00 84041 LJ95 G-GAIS00 84041 LJ13 G-GAIS00 84041 LJ34	Year/Term	2022 ・ Second semester
Number of Credits	2	Course Type	Lecture
Target Year	From 1st to 3rd year students	Target Student
Language	Japanese	Day/Period	Mon.2
Instructor name	ZHAO LIANG (Graduate School of Advanced Integrated Studies in Human Survivability Associate Professor)
Outline and Purpose of the Course	What is life? What is Wisdom? This lecture considers these questions with the latest achievements from science and technology including Biology, Physics, Anthropology, Neuroscience, Cognitive Science and Artificial Intelligence. A common phenomenon is shown in the nature of life and the prosperity of Human (i.e., Homo Sapiens), fragmentation and integration of science, disruption and organization of society, research, education, innovation and future life, which can be described using information and information entropy. It is discussed in this lecture that wisdom is better defined as information processing to reduce information entropy which evolves by learning and random selecting.
Course Goals	1. Learn fundamental knowledge of Neuroscience, Cognitive Science and Artificial Intelligence and understand the challenges of human society from the past to the future. 2. Can consider the universe, life, evolution, wisdom and learning, etc from the aspect of information. 3. Can study life, future and other matter with this concept of wisdom.
Schedule and Contents	1. Introduction and physics 2. Life and the evolution of life 3. Schrödinger's study on entropy and life 4. Secret behind the life of human (Home Sapiens) 5. Challenge from Artificial Intelligence 6. Dissipative structure and self organization 7. Demon of Maxwell, information and energy 8. What is wisdom 9. Creation and learning, random selecting 10. Free energy principle - frontier in brain science 11. AI ethics and the Trolley problem 12. (Technology) Singularity and SuperIntelligence 13. Life 3.0, future wisdom 14. Other related issues 15. Student presentation Remark: the above content may change according to learning attainment
Evaluation Methods and Policy	participant 30%, presentation 30%, report 40%
Course Requirements	It is not required to know Information Science before the lecture but self-learning is strongly recommended.
Study outside of Class (preparation and review)	Off-campus report may be given.